Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR

TL;DR

The paper tackles how to satisfy data subjects' informational and actionable needs under the GDPR without opening the black box of automated decisions. It proposes unconditional counterfactual explanations as a robust, threefold support: understanding why a decision occurred, providing grounds to contest it, and showing what would need to change to obtain a desired outcome in the future, all without disclosing proprietary logic. The authors ground their approach in history of knowledge, causality, and fairness and argue it better aligns with GDPR constraints while enhancing trust and accountability. This approach offers a practical path to credible, actionable explanations that protect trade secrets while empowering individuals.

Abstract

There has been much discussion of the right to explanation in the EU General Data Protection Regulation, and its existence, merits, and disadvantages. Implementing a right to explanation that opens the black box of algorithmic decision-making faces major legal and technical barriers. Explaining the functionality of complex algorithmic decision-making systems and their rationale in specific cases is a technically challenging problem. Some explanations may offer little meaningful information to data subjects, raising questions around their value. Explanations of automated decisions need not hinge on the general public understanding how algorithmic systems function. Even though such interpretability is of great importance and should be pursued, explanations can, in principle, be offered without opening the black box. Looking at explanations as a means to help a data subject act rather than merely understand, one could gauge the scope and content of explanations according to the specific goal or action they are intended to support. From the perspective of individuals affected by automated decision-making, we propose three aims for explanations: (1) to inform and help the individual understand why a particular decision was reached, (2) to provide grounds to contest the decision if the outcome is undesired, and (3) to understand what would need to change in order to receive a desired result in the future, based on the current decision-making model. We assess how each of these goals finds support in the GDPR. We suggest data controllers should offer a particular type of explanation, unconditional counterfactual explanations, to support these three aims. These counterfactual explanations describe the smallest change to the world that can be made to obtain a desirable outcome, or to arrive at the closest possible world, without needing to explain the internal logic of the system.